@inproceedings{parikh-etal-2020-irlab-daiict,
    title = "{IRL}ab{\_}{DAIICT} at {S}em{E}val-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification",
    author = "Parikh, Apurva  and
      Bisht, Abhimanyu Singh  and
      Majumder, Prasenjit",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.264/",
    doi = "10.18653/v1/2020.semeval-1.264",
    pages = "2006--2011",
    abstract = "The paper describes systems that our team IRLab{\_}DAIICT employed for shared task OffensEval2020: Multilingual Offensive Language Identification in Social Media shared task. We conducted experiments on the English language dataset which contained weakly labelled data. There were three sub-tasks but we only participated in sub-tasks A and B. We employed Machine learning techniques like Logistic Regression, Support Vector Machine, Random Forest and Deep learning techniques like Convolutional Neural Network and BERT. Our best approach achieved a MacroF1 score of 0.91 for sub-task A and 0.64 for sub-task B."
}